一个深层的神经网络用于在开放场景中的触觉感知
Huirong Fang1,2, Qianhui Yang3, Kunhong Liu3,4
1School of Electronic Information, Zhangzhou Institute of Technology, Zhangzhou 363000, China.
iScience
|May 9, 2025
概括
这项研究引入了一种新方法,用于在开放环境中识别机器人的触觉材料,克服数据变异. 多感应场注意力增强网络 (MRFE) 改善了机器人的感知,尽管条件发生了变化.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 触觉感知对于机器人与环境互动至关重要.
- 现实世界中的机器人应用面临着诸如意想不到的环境变化等挑战,导致数据变化和触觉感知方面的困难.
- 现有的触觉感知研究往往侧重于封闭的环境,限制了对开放场景的适用性.
研究的目的:
- 为机器人解决在开放场景中触摸材料识别的挑战.
- 开发一种能够处理外部条件变化引起的数据漂移的方法.
- 在动态环境中增强机器人触觉感知的稳定性.
主要方法:
- 构建跨批次触觉数据集以模拟开放场景条件.
- 关于多感应场注意力增强网络 (MRFE) 的建议,用于触觉材料识别.
- 与其他机器学习算法进行实验比较.
主要成果:
- 拟议的MRFE方法有效地克服了数据漂移问题.
- 该系统展示了改进的触觉材料识别,尽管姿势,接触力,滑动速度,探索运动和组装条件的变化.
- 实验结果显示,与现有的机器学习算法相比,性能优越.
结论:
- 在开放的机器人场景中,MRFE网络为触摸材料识别提供了强大的解决方案.
- 这种方法提高了机器人的适应性和可靠性,在现实世界中,动态的环境.
- 该研究有助于推进实际应用的机器人触觉感知能力.
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